vignettes/fticRanalysis.Rmd
fticRanalysis.RmdFourier-transform ion cyclotron resonance (FTICR) mass spectrometry is a type of mass spectrometery for determining the mass-to-charge ratio (m/z) of ions based on the cyclotron frequency of the ions in a fixed magnetic field.
Chemical composition can be determined for a portion of the observed peaks/mass-to-charge ratios.
Data resulting from FTICR instrument runs can be interpreted as the area under a peak for each observed peak. These are comparable in magnitude within a sample but are not comparable across samples.
The fticRanalysis package was designed to help with various steps of processing FTICR data, including:
We have found that most ’omics data generated by EMSL can be divided into three parts:
The edata object is a data frame with one row per peak and one column per sample. It must have one column that is a unique ID (e.g. Mass).
library(fticRanalysis)
data("fticr12T_edata")
str(fticr12T_edata)## 'data.frame': 24442 obs. of 21 variables:
## $ Mass : num 98.5 98.8 98.8 101.7 103.3 ...
## $ EM0011_sample: num 0 0 5524739 0 0 ...
## $ EM0013_sample: num 0 13070372 0 0 0 ...
## $ EM0015_sample: num 0.0 0.0 2.4e+07 0.0 0.0 ...
## $ EM0017_sample: num 0 16120890 0 0 0 ...
## $ EM0019_sample: num 0 21228496 0 0 0 ...
## $ EM0061_sample: num 1197974 0 30656158 0 0 ...
## $ EM0063_sample: num 0 12305626 0 0 0 ...
## $ EM0065_sample: num 0.0 1.1e+07 0.0 0.0 0.0 ...
## $ EM0067_sample: num 0 0 12664590 0 0 ...
## $ EM0069_sample: num 2535836 38329628 0 0 0 ...
## $ EW0111_sample: num 0 0 21416774 0 0 ...
## $ EW0113_sample: num 0 8070914 0 0 0 ...
## $ EW0115_sample: num 3636046 0 38608164 0 0 ...
## $ EW0117_sample: num 0 3965230 0 0 0 ...
## $ EW0119_sample: num 0 0 2439325 0 1153547 ...
## $ EW0161_sample: num 0 0 0 0 0 0 0 0 0 0 ...
## $ EW0163_sample: num 0 0 0 0 0 0 0 0 0 0 ...
## $ EW0165_sample: num 0 0 0 16443347 0 ...
## $ EW0167_sample: num 0 1598118 0 0 0 ...
## $ EW0169_sample: num 0 0 0 0 0 0 0 0 0 0 ...
The fdata object is a data frame with one row per sample with information about experimental conditions. It must have a column that matches the sample column names in edata.
data("fticr12T_fdata")
str(fticr12T_fdata)## 'data.frame': 20 obs. of 4 variables:
## $ SampleID : Factor w/ 20 levels "EM0011_sample",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Location : Factor w/ 2 levels "M","W": 1 1 1 1 1 1 1 1 1 1 ...
## $ Block : int 1 2 3 4 5 1 2 3 4 5 ...
## $ Crop.Flora: Factor w/ 2 levels "C","S": 2 2 2 2 2 1 1 1 1 1 ...
The emeta object is a data frame with one row per peak and columns containing other meta data such as molecular formula or elemental columns. It must have an ID column corresponding to the ID column in edata.
data("fticr12T_emeta")
str(fticr12T_emeta)## 'data.frame': 24442 obs. of 10 variables:
## $ Mass : num 98.5 98.8 98.8 101.7 103.3 ...
## $ C : int 0 0 0 0 0 0 0 0 0 0 ...
## $ H : int 0 0 0 0 0 0 0 0 0 0 ...
## $ O : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N : int 0 0 0 0 0 0 0 0 0 0 ...
## $ C13 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ S : int 0 0 0 0 0 0 0 0 0 0 ...
## $ P : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Error : num 0 0 0 0 0 0 0 0 0 0 ...
## $ NeutralMass: num 99.5 99.8 99.8 102.7 104.4 ...
picr <- as.peakIcrData(fticr12T_edata, fticr12T_fdata, fticr12T_emeta,
edata_cname="Mass", fdata_cname="SampleID",
mass_cname="Mass", c_cname="C", h_cname="H",
o_cname="O", n_cname="N", s_cname="S",
p_cname="P", isotopic_cname = "C13",
isotopic_notation = "1")
picr## peakIcrData object
## # Peaks: 23060
## # Samples: 20
## Meta data columns: [Mass, C, H, O, N, C13, S, P, Error, NeutralMass, MolForm]
The peakIcrData object contains three elements, named e_data, f_data, and e_meta:
names(picr)## [1] "e_data" "f_data" "e_meta"
During object constructing, the molecular formula is calculated from the elemental columns:
tail(picr$e_meta)## Mass C H O N C13 S P Error NeutralMass MolForm
## 24437 897.1796269 0 0 0 0 0 0 0 0.0000000 898.1869 <NA>
## 24438 897.2209292 0 0 0 0 0 0 0 0.0000000 898.2282 <NA>
## 24439 897.3973977 36 69 22 1 0 0 1 0.2345417 898.4047 C36H69NO22P
## 24440 898.812526 0 0 0 0 0 0 0 0.0000000 899.8198 <NA>
## 24441 899.0458907 0 0 0 0 0 0 0 0.0000000 900.0532 <NA>
## 24442 899.3370941 0 0 0 0 0 0 0 0.0000000 900.3444 <NA>
When dealing with ’omics data quantitatively, we often log-transform to stabilize variances and reduce skew for downstream statistics.
It’s common to treat FTICR data as presence/absence data
picr <- edata_transform(picr, data_scale="log2")Or replace 0s with NAs
edata_replace(picr, x = 0, y = NA)NOSC, aromaticity, O:C and H:C ratios, etc.
picr <- compound_calcs(picr)
picr## peakIcrData object
## # Peaks: 23060
## # Samples: 20
## Meta data columns: [Mass, C, H, O, N, C13, S, P, Error, NeutralMass, MolForm, AI, AI_Mod, DBE, DBE_O, DBE_AI, GFE, kmass, kdefect, NOSC, OtoC_ratio, HtoC_ratio, NtoC_ratio, PtoC_ratio, NtoP_ratio]
bs1 - Kim, S., et al (2003). Analytical Chemistry.bs2 - Bailey, V. et al (2017). Soil Biology and Biochemistry.bs3 - Rivas-Ubach, A., et al (2018). Analytical chemistry. – coming soon
picr <- assign_class(picr, boundary_set = "bs1")
table(picr$e_meta$Class)## < table of extent 0 >
There are multiple types of filtering algorithms provided in fticRanalysis:
filter_obj <- mass_filter(picr)
plot(filter_obj, min_mass=200, max_mass=900)summary(picr)## Samples: 20
## Molecules: 23060
## Percent Missing: 81.739%
picr <- applyFilt(filter_obj, picr, min_mass = 200,
max_mass = 900)
summary(picr)## Samples: 20
## Molecules: 19327
## Percent Missing: 79.299%
Other filtering options include number of molecule observations, formula presence or absence, or emeta columns.
picr <- applyFilt(molecule_filter(picr), picr, min_num=2)
picr <- applyFilt(formula_filter(picr), picr)
picr <- applyFilt(emeta_filter(picr, "NOSC"), picr, min_val = 0.5)
summary(picr)## Samples: 20
## Molecules: 1521
## Percent Missing: 50.352%
one_sample <- subset(picr, samples="EM0011_sample")
summary(one_sample)## Samples: 1
## Molecules: 1521
## Percent Missing: 57.791%
head(one_sample$e_data)## Mass EM0011_sample
## 3746 200.9433045 NA
## 3748 200.9863723 NA
## 3774 202.9413892 NA
## 3844 209.0091827 20.32056
## 3892 212.0200788 20.82018
## 3909 213.004142 NA
A Van Krevelen plot shows H:C ratio vs O:C ratio for each peak observed in a sample that has a mass formula (thus H:C and O:C are known). By default, the points are colored according to functional class.
vanKrevelenPlot(one_sample, title="EM0011_sample")By default, this function colors by Van Krevelen class. However, we can also color the points according to other meta data columns in the e_meta object.
vanKrevelenPlot(one_sample, colorCName="PtoC_ratio",
title="Color by P:C Ratio", legendTitle = "P:C Ratio")We can also plot the distributions of any (numeric) column of meta-data (i.e. column of e_meta).
densityPlot(one_sample, variable = "NOSC", plot_curve=TRUE, plot_hist=TRUE,
title="NOSC Distribution for EM0011_sample")It’s also possible to plot just the histogram or just the density curve with this function with the plot_hist and plot_curve parameters.
densityPlot(one_sample, variable = "kmass",
title="Kendrick Mass for EM0011_sample", plot_hist=TRUE,
plot_curve = FALSE, yaxis="count")A Kendrick plot shows Kendrick Defect vs Kendrick mass for each observed peak.
Ions of the same family have the same Kendrick mass defect and are positioned along a horizontal line on the plot. Kendrick plot is often used in conjunction with a Van Krevelen plot for evaluating elemental composition.
kendrickPlot(one_sample, title="Kendrick Plot for EM0011_sample")The goal of this experiment was to identify differences in soil organic matter between sample locations and crop types.
In order to do that we need to compare experimental treatments (groups).
The group_designation method defines treatment groups based on the variable(s) specified as main effects.
picr <- group_designation(picr, main_effects=c("Crop.Flora"))
getGroupDF(picr)## SampleID Group
## 1 EM0011_sample S
## 2 EM0013_sample S
## 3 EM0015_sample S
## 4 EM0017_sample S
## 5 EM0019_sample S
## 6 EM0061_sample C
## 7 EM0063_sample C
## 8 EM0065_sample C
## 9 EM0067_sample C
## 10 EM0069_sample C
## 11 EW0111_sample C
## 12 EW0113_sample C
## 13 EW0115_sample C
## 14 EW0117_sample C
## 15 EW0119_sample C
## 16 EW0161_sample S
## 17 EW0163_sample S
## 18 EW0165_sample S
## 19 EW0167_sample S
## 20 EW0169_sample S
The summarizeGroups function calculates group-level summaries per peak, such as the number or proportion of samples in which peak is observed. The resulting object’s e_data element contains one column per group, per summary function.
group_summary <- summarizeGroups(picr, summary_functions =
c("n_present", "prop_present"))
head(group_summary$e_data)## Mass S_n_present S_prop_present C_n_present C_prop_present
## 1 200.9433045 3 0.3 4 0.4
## 2 200.9863723 2 0.2 0 0.0
## 3 202.9413892 1 0.1 1 0.1
## 4 209.0091827 10 1.0 7 0.7
## 5 212.0200788 9 0.9 9 0.9
## 6 213.004142 2 0.2 3 0.3
densityPlot(picr, samples=FALSE, groups=c("S","C"), variable="NOSC",
title="Comparison of NOSC Between Crop Types") Create peakIcrData objects that each contain two groups to facilitate group comparisons
byGroup <- divideByGroupComparisons(picr,
comparisons = "all")[[1]]$value
crop_unique <- summarizeGroupComparisons(byGroup,
summary_functions="uniqueness_gtest",
summary_function_params=list(
uniqueness_gtest=list(pres_fn="nsamps",
pres_thresh=2, pvalue_thresh=0.05)))
tail(crop_unique$e_data)## Mass uniqueness_gtest
## 1516 653.0299655 <NA>
## 1517 657.0215477 <NA>
## 1518 658.1682292 Observed in Both
## 1519 658.1686302 Observed in Both
## 1520 700.9749846 <NA>
## 1521 719.098301 <NA>
vanKrevelenPlot(crop_unique, colorCName = "uniqueness_gtest")